Workers’ compensation costs continue to rise due to increasing medical and prescription drug expenses, and escalating treatment options. According to the National Academy of Social Insurance, 33 states spent more than half of their workers’ compensation benefits on medical care costs. Medical costs now account for 60 percent of all workers’ compensation claims. Businesses and workers’ compensation insurers are looking to big data technologies to find solutions to help alleviate the rising price tag of medical costs.
Fortunately, most businesses and insurers have an abundance of big data to work with. When used strategically, data analytics, combined with emerging artificial intelligence and machine learning technologies, can be an effective tool for controlling the rising tide of workers’ compensation medical costs. Data analytics can be used to identify and intervene on claims that are driving those costs.
Big data can help injured workers get the right help at the right time as well as identify their need for resources throughout their recovery process. Businesses need to develop models that will analyze the data collected in order to:
- Gain a deeper understanding of the issues the worker is facing
- Quickly establish the best treatment plan for each individual worker
- Assign the appropriate people and identify the proper resources to achieve the best possible outcome.
When a worker becomes injured, getting them healed and back to work becomes the priority. Big data helps businesses and insurers deliver better claim outcomes for the injured employee. But in order to effectively use the data that is collected, a model must be built that will help decision makers understand the issues that drive costs as well as develop processes that make the information generated actionable. An effective analytical model must contain three elements to better help businesses control workers’ compensation costs.
Is the claim compensable? Not all injuries that occur at work qualify for worker’s compensation benefits. Big data can be used to analyze the probability a claim will be covered. Early identification of ineligible cases is in everyone’s best interest. Data analytics can be used to assess each situation using a sophisticated algorithm that will flag claims for further investigation.
2. High-Cost Claims
In addition to identifying ineligible claims, an escalation alert model can be built that will identify high-cost claims early on, allowing staff to intervene sooner. This type of model should identify co-morbidities (the presence of two or more health issues) which could affect a workers’ recovery timeline as well as any additional resources and support that may be needed.
3. Return-to-Work Efforts
Most workers’ compensation claims are straightforward and do not require special handling. However, a percentage of cases require referrals or case management services. Complex cases need to be differentiated from simple claims so that medical expertise can be brought in early, thus reducing overall claim costs as well as accelerating a worker’s return to work. For example, some injured employees will heal faster if they are provided a nurse during the recovery process.
There are many issues that can impact workers’ compensation claims and the recovery process for injured workers. When employers employ big data analytic technologies to put their data to good use, employees and the company will benefit.
Workers’ compensation insurance requires highly-trained professionals that understand the complexities, regulations and laws that pertain to the worker’s compensation system. Marsh & McLennan Agency (MMA) is a trusted partner to help your organization mitigate costs and claim outcomes. Contact us here to learn more about our workers’ compensation insurance expertise and services.